How to Fix Names, Acronyms, and Brand Terms in AI Transcripts (Without Breaking Everything)

To fix names, acronyms, and brand terms in AI transcripts, you need two things: an authoritative list of the “right” spellings and a safe way to apply them without creating new errors. The fastest approach is to build a small glossary, confirm spellings from reliable sources, add phonetic hints for tricky audio, and use careful find/replace rules that avoid partial-word mistakes.

This guide walks through a practical workflow you can use for meetings, podcasts, webinars, and technical calls, including ways technical teams can keep product, competitor, and industry terms accurate over time.

Primary keyword: fix names acronyms brand terms in AI transcripts

Key takeaways

• Start by collecting authoritative spellings (not guesses) for people, companies, products, and acronyms.
• Build a living glossary with preferred casing, punctuation, and “do-not-change” notes.
• Use phonetic hints to prevent repeated mistakes, especially for names and jargon.
• Apply corrections with safe find/replace patterns that match whole words and protect edge cases.
• For technical teams, keep terms in version control and add new releases and competitor names in one place.

Why AI transcripts struggle with names, acronyms, and brand terms

Speech recognition models do well on common words, but they often miss proper nouns and domain terms because they have less training exposure and more audio variation. One person says “Kubernetes,” another says “koo-ber-NET-eez,” and your AI might output “cuber net ease.”

Acronyms add another layer because “S-O-C” could be “SOC,” “SoC,” or “sock,” depending on context and speaker. Brand terms can also be intentionally stylized (like “iPhone” or “GitHub”), and AI often flattens that styling.

• Names: multiple valid spellings, accents, and uncommon surnames.
• Acronyms: can be spelled out, said as a word, or confused with normal words.
• Brands/products: case- and punctuation-sensitive, sometimes intentionally “wrong” by normal grammar rules.
• Audio realities: crosstalk, low mics, compressed calls, and background noise.

Step 1: Build a glossary that editors can actually use

A glossary is the fastest way to stop the same mistakes from repeating across episodes, meetings, or sprint demos. Keep it lightweight so people maintain it, but specific enough that it answers “what should this be?” in one line.

What to include in a transcript glossary

• Term: the correct spelling as it should appear in the transcript.
• Type: person, company, product, feature, acronym, protocol, competitor, team name.
• Variants: common wrong outputs from your AI (or common misspellings).
• Case/punctuation: e.g., “GitHub,” “Figma,” “C++,” “E2E,” “SOC 2.”
• Context note: one short clue that helps disambiguate (e.g., “SOC 2 = compliance report”).
• Do-not-change: terms that must remain exact (legal entities, trademarks, personal names).

Easy formats that work

• CSV/Google Sheet: best for quick editing and sharing with vendors.
• YAML/JSON: best if you want to automate checks in a pipeline.
• Markdown table: best for internal docs and PR reviews.

If you do any automation, choose a stable “source of truth” format (often a CSV or YAML) and generate the rest from it. That prevents one-off edits from drifting.

Step 2: Collect authoritative spellings (and don’t rely on memory)

The goal is not “a reasonable guess.” The goal is “what the person or company uses,” so you can defend the spelling later and keep future transcripts consistent.

Good sources for authoritative spellings

• People: their email signature, LinkedIn profile, conference speaker page, or internal directory.
• Companies/brands: official website header/footer, press kit, or trademark/brand guidelines page.
• Products/features: docs site, release notes, in-app UI labels, or the GitHub repo name.
• Acronyms: your internal documentation, standards body docs, or the first time the acronym is defined in your material.

When a term has multiple acceptable styles, pick one and write it down in the glossary. For example, decide whether you want “single sign-on” or “single sign on,” and whether you prefer “Okta” versus “OKTA” when referring to the company in narrative text.

Decide casing and punctuation rules once

• Case: “iPad” vs “IPad,” “macOS” vs “MacOS.”
• Hyphens: “real-time” vs “real time,” “roll-out” vs “rollout.”
• Dots/slashes: “U.S.” vs “US,” “CI/CD” vs “CICD.”
• Numbers: “SOC 2” vs “SOC2,” “Type II” vs “Type 2.”

Consistency matters more than perfection for style choices, but names and legal entities should match the authoritative source.

Step 3: Add phonetic hints so the next transcript starts cleaner

Phonetic hints reduce repeat errors, especially when your AI engine lets you add “custom vocabulary” or “phrase hints.” Even if you can’t configure the model, you can still store phonetic notes for humans and proofreaders.

Where phonetic hints help most

• Uncommon names: “Nguyen,” “Saoirse,” “Joaquin.”
• Non-English brands: terms that get anglicized in many ways.
• Letter/number mixes: “K8s,” “G Suite,” “H.264.”
• Near-homophones: “Kubernetes” vs “Kuber-” outputs, “cache” vs “cash.”

Simple phonetic formats you can use

• Plain English: “Pulumi (poo-LOO-mee).”
• Stress marks: “Datadog (DAY-tuh-dawg).”
• Spelled-out letters: “SRE (ess-are-ee).”

Keep phonetic notes short, and always pair them with the correct spelling. A phonetic note alone can create confusion if the transcript later gets edited by someone new.

Step 4: Correct safely with find/replace patterns (avoid partial-word mistakes)

Find/replace is powerful, but it can also wreck a transcript if you replace inside other words or miss casing variants. Use “whole word” matching, test on a small selection, and protect terms that should not change.

Rule 1: Prefer whole-word matches

If your editor supports regex, use word boundaries when you can. This reduces cases like replacing “Ann” inside “Annex” or “Son” inside “Sonia.”

• Safer pattern (regex): \bAnn\b → An (example only)
• Risky pattern: Ann → An (could change “Annex”)

Rule 2: Protect punctuation and possessives

Names and acronyms often appear with punctuation, plurals, and possessives. Decide what you will match, and include common forms.

• Examples to consider: “Acme,” “Acme’s,” “Acmes,” “Acme,” with comma/period.
• Safer approach: run multiple small replacements instead of one broad one.

Rule 3: Fix one concept at a time

If you try to correct 40 terms in one giant replacement pass, you won’t know which rule caused damage. Batch changes into small groups, then spot-check.

Rule 4: Create a “do-not-change” list

Some strings look like errors but are correct in context, like a last name that is also a normal word. Put these in a do-not-change list and tell editors to skip automated replacements for them.

Rule 5: Watch for acronym collisions

Acronyms can map to different concepts in different teams. “ICE” could be a support queue, a security program, or something unrelated, so don’t expand or restyle it without context.

• Tip: add a short context note in the glossary (one clause is enough).

A practical “safe replace” checklist

• Turn on whole-word match (or regex word boundaries) when possible.
• Replace the most unique terms first (less likely to collide).
• Review each replacement’s preview list before you click “Replace all.”
• After replacing, search again for the old term to confirm you got the right set.
• Scan for suspicious new strings (like “micro soft” becoming “Microsoft” in the wrong place).

Workflow for technical teams: product names, competitor names, and industry terms

Technical orgs often need transcripts for customer calls, sprint reviews, incident postmortems, and training videos. The best workflow keeps terms accurate across all of them, without relying on one person’s memory.

1) Set up a “terms intake” process

• When: every release, new integration, or new competitor mention.
• Who: product marketing, developer relations, or a tech writer can own the list.
• What to submit: term, correct spelling, link to authoritative source, and 1-line context.

2) Put the glossary in version control (if you can)

If your team already uses Git, store the glossary alongside docs, enable pull requests, and require review for changes. That gives you change history and avoids silent edits.

• Example structure: /glossary/transcript-terms.csv and /glossary/phonetics.md

3) Create two tiers: “must be exact” and “nice to have”

• Must be exact: executive names, customer names (when permitted), legal entities, product names, competitor brands, compliance terms.
• Nice to have: casual internal nicknames, temporary project codenames, slang.

This keeps proofreading time focused where mistakes create real confusion or risk.

4) Add competitor names deliberately

Competitor terms often appear in comparisons, and the transcript should reflect what was said without turning into a branding mess. Capture competitor spellings exactly as they present themselves, and don’t “correct” them into your preferred style.

• Include: official spelling, common mishearing, and category (competitor).
• Note: avoid subjective notes like “bad product,” and stick to spelling guidance.

5) Standardize how you treat acronyms

Choose a rule and apply it across transcripts, such as “spell out on first use, then use the acronym,” or “keep acronyms as spoken.” Your choice depends on the transcript’s purpose.

• For external audiences: consider “first mention expanded,” when you can do it accurately.
• For internal search: keep acronyms consistent so people can find content fast.

6) Add a quick QA step before publishing

• Search for top 20 critical terms (product, CEO, flagship feature, main competitors).
• Skim the first mention of every acronym to confirm it matches the intended meaning.
• Check speaker labels around key decisions to avoid attributing a statement to the wrong person.

If you also generate captions or subtitles, run the same terminology checks before you publish video assets. GoTranscript offers dedicated closed captioning services if you need formatted caption files.

Common pitfalls (and how to avoid them)

• Overcorrecting common words: if a name is also a normal word (like “Will”), treat it as a special case and avoid global replacements.
• Fixing the wrong acronym: add context notes and confirm meaning from surrounding sentences.
• Breaking other terms: a short string like “Net” can appear inside dozens of words, so avoid replacing short fragments.
• Inconsistent casing: decide whether you want “API” vs “Api,” then enforce it through the glossary.
• Skipping source links: without a source, future editors will “correct” your correction.

Common questions

Should I correct brand capitalization in a verbatim transcript?

Usually yes, because capitalization and punctuation are part of the official name and improve readability. If you need strict verbatim rules, write that policy down and apply it consistently.

How do I handle acronyms that can mean two different things?

Add a context note in your glossary and confirm the meaning from the surrounding discussion. If the audio is unclear, flag it for human review instead of guessing.

What’s the safest way to use find/replace for names?

Use whole-word matching (or regex word boundaries), preview every change, and replace in small batches. Avoid replacing short strings that could appear inside other words.

How many terms should go in my glossary?

Start with 20–50 high-impact terms: leaders, product names, core features, key customers (if allowed), and common acronyms. Add new terms as they come up rather than trying to boil the ocean.

Do I need phonetic hints if I plan to have humans proofread?

Phonetic hints still help because they reduce ambiguity and speed up review. They are most useful for uncommon names and technical jargon.

How do technical teams keep terminology updated across releases?

Use an intake process tied to releases and store the glossary in a shared place, ideally with change history like version control. Require a quick review when a new product, integration, or competitor enters the conversation.

What reference materials help a transcriptionist the most?

A short glossary, a speaker list with names and roles, and links to official product pages or docs usually help more than long background decks. If you have a list of “known bad” AI outputs, include that too.

How to provide reference materials for accurate human transcription or proofreading

If you plan to move from AI-only to human review, your glossary becomes your main handoff document. Send it alongside the audio and specify what “correct” means for your use case.

• Glossary: preferred spellings, casing, acronyms, and competitor names.
• Speaker list: full names, titles, and any tricky pronunciations.
• Links: official product pages, docs, or brand guidelines for authoritative spellings.
• Style notes: verbatim vs cleaned-up, how to treat filler words, and acronym policy.
• “Do-not-change” list: legal entities, trademarks, and sensitive terms.

If you already have an AI transcript, consider sending it for human cleanup instead of starting from scratch. A focused pass can fix names, acronyms, and terminology while keeping timing and structure intact, especially if you pair it with a glossary and clear instructions.

When you want transcripts that read cleanly and keep names and brand terms consistent, GoTranscript can help with both AI and human options, including transcription proofreading services when you already have a draft. If you’re ready to hand off audio and your terminology references, explore GoTranscript’s professional transcription services to match the workflow that fits your team.